Barriers of Urban Mobility

Gergő Pintér1 and Balázs Lengyel1,2,3

1ANETI Lab, Corvinus University of Budapest
2ANETI Lab, HUN-REN Center for Economic and Regional Sciences
3Institute for Data Analytics and Information Systems, Corvinus University of Budapest

motivation

Nagykőrösi road, Budapest by vst via Mapillary CC BY-SA 4.0

mobile positioning data

  • collected from various, unspecified smartphone apps
    • timestamp, user ID, location
    • GPS-based location
  • pings are clustered into stops (Juhász et al., 2023)
    • using Infostop algorithm (Aslak & Alessandretti, 2020)
    • where some time was spent

building a network

the blocks are considered nodes
connected if a user had consecutive stops in two blocks within a day

community detection

  • using the network built from the stops
  • Louvain community detection is applied
    • with different resolution values
    • executed 10 times for each resolution
Louvain communities (resolution 2.5)

Louvain community detection - resolution 2.5

infrastructural barriers: primary and secondary (dotted) roads
administrative boundaries: districts and neighborhood (dotted)

barrier crossing ratio

BCRγbarrier=1n∑mCB∑mCB×CCγ BCR_{\gamma}^{barrier} = \dfrac{1}{n} \frac{ \sum_{m} \text{{CB}} }{ \sum_{m} \text{{CB}} \times \text{{CC}}_{\gamma} }

  • m is the number of mobility edges
  • γ\gamma is the resolution
  • n is the number of iterations at resolution γ\gamma

by barrier types:

  • district
  • neighborhood
  • primary roads
  • secondary
  • railways
  • river

BCR by barrier types

classify users based on home location

trips within Budapest are considered

but the classification is not restricted to Budapest

decomposing barrier crossing ratio

decomposing barrier crossing ratio

thanks for the attention!

Gergő Pintér, gergo.pinter @ uni-corvinus.hu, @pintergreg

this presentation is available online: pintergreg.github.io/ccs24/short

references

Aslak, U., & Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. https://arxiv.org/abs/2003.14370
Juhász, S., Pintér, G., Kovács, Á. J., Borza, E., Mónus, G., Lőrincz, L., & Lengyel, B. (2023). Amenity complexity and urban locations of socio-economic mixing. EPJ Data Science, 12(1), 34.

already available on arXiv:
2312.11343